In view of inconsistent face acquisition procedure in face anti-spoofing, the detection performance on the target domain generally suffers severe degradation under source-specific gradient optimization. Existing domain adaptation face anti-spoofing methods focus on improving model generalization capability through feature matching, which do not consider the gradient discrepancy between the source and target domains. To this end, this work develops a category-conditional gradient alignment guided face anti-spoofing algorithm (CCGA-FAS) from a novel perspective of gradient discrepancy elimination. Technically, the category-conditional gradient alignment mechanism maximizes the cosine similarity of the gradient vectors generated by source and target samples within the live and spoof categories separately, which promotes the source and target domains to follow similar gradient descent directions during optimization. Considering that the gradient vector generation and alignment is computationally dependent on reliable category information, a temporal knowledge and flexible threshold based dynamic category measurer is devised to provide pseudo category information for unlabelled target samples in an easy-to-hard manner. The optimization for CCGA-FAS is implemented under the teacher-student structure, where the student model serves as the gradient optimization backbone, and the category prediction simultaneously benefits from the teacher and student models to consolidate the alignment stability. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods in both unsupervised and K-shot semi-supervised domain adaptive face anti-spoofing scenarios.

中文翻译：

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用于域自适应人脸反欺骗的类别条件梯度对齐


鉴于人脸反欺骗中人脸采集程序不一致的问题，在源特异性梯度优化下，目标域的检测性能普遍严重下降。现有的域适配面反欺骗方法侧重于通过特征匹配来提高模型泛化能力，没有考虑源域和目标域之间的梯度差异。为此，这项工作从梯度差异消除的新角度开发了一种类别条件梯度对齐引导人脸反欺骗算法 （CCGA-FAS）。从技术上讲，类别条件梯度对齐机制最大限度地提高了源样本和目标样本分别在 live 和 spoof 类别中生成的梯度向量的余弦相似性，从而促进源域和目标域在优化过程中遵循相似的梯度下降方向。考虑到梯度向量的生成和对齐在计算上依赖于可靠的类别信息，设计了一种基于时间知识和灵活阈值的动态类别测量器，以易懂的方式为未标记的目标样本提供伪类别信息。CCGA-FAS 的优化是在师生结构下实现的，其中学生模型作为梯度优化骨干，类别预测同时受益于师生模型，以巩固对齐稳定性。实验结果和分析表明，所提出的方法在无监督和 K-shot 半监督域自适应人脸反欺骗场景中均优于最先进的方法。